Superstore Performance Dataset – Exploratory and Descriptive Analysis

Author

Affiliation

UWERA Bonnefete yvette

Data analyst

Published

June 27, 2025

In this notebook, we perform an in-depth exploratory and descriptive analysis of the Superstore Performance Dataset, a rich dataset capturing retail transaction details across various product categories, regions, and customer segments.

This phase of analysis is crucial for uncovering trends, identifying potential business insights, and gaining a solid understanding of the dataset’s structure before developing visual dashboards or advanced analytics. We explore the distribution of key numerical and categorical variables, examine relationships between sales performance metrics (such as sales, profit, and quantity) and business dimensions (such as region, category, and customer segment), and use visualizations to highlight important patterns. Particular focus is placed on sales performance across regions, product categories, and return behavior, helping to build a strong foundation for actionable recommendations and strategic decision-making.

We begin our analysis by importing the core Python libraries required for data processing, numerical computation, visualization, and directory management:

pandas: Enables efficient manipulation, filtering, and aggregation of structured tabular data, forming the backbone of our analysis pipeline.
numpy: Provides support for numerical operations, array-based computations, and statistical summaries.
os: Facilitates interaction with the file system, allowing us to build flexible and portable directory paths for data and output management.
plotly.express: A high-level graphing library that enables creation of interactive, publication-quality visualizations, which we use extensively to uncover patterns and present insights throughout the notebook.

Code

# Import libraries

import pandas as pd
import numpy as np
import os
import plotly.express as px

0.1 Define and Create Directory Paths

To ensure reproducibility and organized storage, we programmatically create directories if they don’t already exist for:

raw data
processed data
results
documentation

These directories will store intermediate and final outputs for reproducibility.

0.2 Loading the Cleaned Dataset

We load the cleaned version of the Superstore Performance Dataset from the processed data directory into a Pandas DataFrame. The head(10) function displays the first ten records, providing a quick look at key columns such as Customer ID, Segment, Country, Category, Sales, Profit, and Returned.

Code

store_data_filename = os.path.join(processed_dir, "SuperStore-Cleaned.csv")
super_df = pd.read_csv(store_data_filename)
super_df.head(10)

superstore performance dashboard
	Row ID	Order ID	Order Date	Ship Date	Ship Mode	Customer ID	Customer Name	Segment	Country	City	...	Product ID	Category	Sub-Category	Product Name	Sales	Quantity	Discount	Profit	Returned	Person
0	1	CA-2016-152156	2016-11-08	2016-11-11	Second Class	CG-12520	Claire Gute	Consumer	United States	Henderson	...	FUR-BO-10001798	Furniture	Bookcases	Bush Somerset Collection Bookcase	261.9600	2	0.00	41.9136	NO	Cassandra Brandow
1	2	CA-2016-152156	2016-11-08	2016-11-11	Second Class	CG-12520	Claire Gute	Consumer	United States	Henderson	...	FUR-CH-10000454	Furniture	Chairs	Hon Deluxe Fabric Upholstered Stacking Chairs,...	731.9400	3	0.00	219.5820	NO	Cassandra Brandow
2	3	CA-2016-138688	2016-06-12	2016-06-16	Second Class	DV-13045	Darrin Van Huff	Corporate	United States	Los Angeles	...	OFF-LA-10000240	Office Supplies	Labels	Self-Adhesive Address Labels for Typewriters b...	14.6200	2	0.00	6.8714	NO	Anna Andreadi
3	4	US-2015-108966	2015-10-11	2015-10-18	Standard Class	SO-20335	Sean O'Donnell	Consumer	United States	Fort Lauderdale	...	FUR-TA-10000577	Furniture	Tables	Bretford CR4500 Series Slim Rectangular Table	957.5775	5	0.45	-383.0310	NO	Cassandra Brandow
4	5	US-2015-108966	2015-10-11	2015-10-18	Standard Class	SO-20335	Sean O'Donnell	Consumer	United States	Fort Lauderdale	...	OFF-ST-10000760	Office Supplies	Storage	Eldon Fold 'N Roll Cart System	22.3680	2	0.20	2.5164	NO	Cassandra Brandow
5	6	CA-2014-115812	2014-06-09	2014-06-14	Standard Class	BH-11710	Brosina Hoffman	Consumer	United States	Los Angeles	...	FUR-FU-10001487	Furniture	Furnishings	Eldon Expressions Wood and Plastic Desk Access...	48.8600	7	0.00	14.1694	NO	Anna Andreadi
6	7	CA-2014-115812	2014-06-09	2014-06-14	Standard Class	BH-11710	Brosina Hoffman	Consumer	United States	Los Angeles	...	OFF-AR-10002833	Office Supplies	Art	Newell 322	7.2800	4	0.00	1.9656	NO	Anna Andreadi
7	8	CA-2014-115812	2014-06-09	2014-06-14	Standard Class	BH-11710	Brosina Hoffman	Consumer	United States	Los Angeles	...	TEC-PH-10002275	Technology	Phones	Mitel 5320 IP Phone VoIP phone	907.1520	6	0.20	90.7152	NO	Anna Andreadi
8	9	CA-2014-115812	2014-06-09	2014-06-14	Standard Class	BH-11710	Brosina Hoffman	Consumer	United States	Los Angeles	...	OFF-BI-10003910	Office Supplies	Binders	DXL Angle-View Binders with Locking Rings by S...	18.5040	3	0.20	5.7825	NO	Anna Andreadi
9	10	CA-2014-115812	2014-06-09	2014-06-14	Standard Class	BH-11710	Brosina Hoffman	Consumer	United States	Los Angeles	...	OFF-AP-10002892	Office Supplies	Appliances	Belkin F5C206VTEL 6 Outlet Surge	114.9000	5	0.00	34.4700	NO	Anna Andreadi

10 rows × 23 columns

Here, we examine the structure of the Superstore dataset:

The dataset contain 9,994 ** entries and 23** variables.
It includes both numerical variables (e.g., Sales, Profit, Quantity, Discount) and categorical variables (e.g., Customer ID, Segment, Region, Category, Returned).

Understanding the data types and identifying any null entries is essential before performing detailed analysis, as it guides data cleaning, transformation, and visualization decisions.

We also inspect the dataset shape. We see that the data has 9,994 rows and 23 columns.

Code

super_df.shape

In addition, we check the data types using .info.

Code

super_df.info()

0.3 Summary Statistics: Numerical Variables

This summary provides a snapshot of the distribution and spread of key numerical variables in the Superstore dataset. Below are the main insights:

Sales values range fom $ 0.4 4 to $22,63848, with a meanof $22.86. The large gap between the mean and maximum suggests a right-skewed distribution, where a few high-value sales significantly impact the average. This is typical in retail, where large orders or bulk items create outliers.
Profit also exhibits a wide range,from –$6,599.98 (a oss)* to $8,399.98 (again). The mean proft is $8 .66**, with a standard deviation of ver *$ 234**, indicating large variability. The presence of negative values shows that some transactions resulted in financial losses, possibly due to heavy discounts, returns, or operational costs exceeding sales.
Discount values range between 0.0 and 0.8, with a mean of 0.16. This suggests that most transactions occur with low to moderate discount rates. The 25th percentile is 0, which indicates that many orders are sold at full price, while a portion receives promotional pricing.
Quantity of products sold per transaction ranges from 1 to 14, with a median of 3 units. Most purchases involve small quantities, reflecting individual or household-level buying behavior.
Postal Code, while numeric in format, represents geographical location and is more appropriately treated as a categorical or identifier column rather than a variable for numerical analysis.
Row ID is a simple index from 1 to 9994, primarily used for tracking rows and not relevant for analysis.

0.4 Descriptive Statistics Table (for key numerical variables)

Variable	Min	25%	Median	Mean	75%	Max
Sales	0.44	17.28	54.49	229.86	209.94	22,638.48
Quantity	1	2	3	3.79	5	14
Discount	0.0	0.0	0.2	0.16	0.2	0.8
Profit	-6,599.98	1.73	8.67	28.66	29.36	8,399.98

Understanding these patterns helps uncover outliers, pricing strategies, and customer purchasing behavior — providing a data-driven foundation for improving performance, managing profit margins, and identifying risk areas in sales. strategies.

Code

super_df.describe()

	Row ID	Postal Code	Sales	Quantity	Discount	Profit
count	9994.000000	9994.000000	9994.000000	9994.000000	9994.000000	9994.000000
mean	4997.500000	55190.379428	229.858001	3.789574	0.156203	28.656896
std	2885.163629	32063.693350	623.245101	2.225110	0.206452	234.260108
min	1.000000	1040.000000	0.444000	1.000000	0.000000	-6599.978000
25%	2499.250000	23223.000000	17.280000	2.000000	0.000000	1.728750
50%	4997.500000	56430.500000	54.490000	3.000000	0.200000	8.666500
75%	7495.750000	90008.000000	209.940000	5.000000	0.200000	29.364000
max	9994.000000	99301.000000	22638.480000	14.000000	0.800000	8399.976000

0.5 Categorical Variables

Code

super_df.describe(include='object')

	Order ID	Order Date	Ship Date	Ship Mode	Customer ID	Customer Name	Segment	Country	City	State	Region	Product ID	Category	Sub-Category	Product Name	Returned	Person
count	9994	9994	9994	9994	9994	9994	9994	9994	9994	9994	9994	9994	9994	9994	9994	9994	9994
unique	5009	1237	1334	4	793	793	3	1	531	49	4	1862	3	17	1850	2	4
top	CA-2017-100111	2016-09-05	2015-12-16	Standard Class	WB-21850	William Brown	Consumer	United States	New York City	California	West	OFF-PA-10001970	Office Supplies	Binders	Staple envelope	NO	Anna Andreadi
freq	14	38	35	5968	37	37	5191	9994	915	2001	3203	19	6026	1523	48	9194	3203

Code

super_df['Ship Mode'].value_counts(normalize=True)

Ship Mode
Standard Class    0.597158
Second Class      0.194617
First Class       0.153892
Same Day          0.054333
Name: proportion, dtype: float64

Code

super_df['Segment'].value_counts(normalize=True)

Segment
Consumer       0.519412
Corporate      0.302181
Home Office    0.178407
Name: proportion, dtype: float64

Code

super_df['City'].value_counts(normalize=True)

City
New York City      0.091555
Los Angeles        0.074745
Philadelphia       0.053732
San Francisco      0.051031
Seattle            0.042826
                     ...   
Glenview           0.000100
Missouri City      0.000100
Rochester Hills    0.000100
Palatine           0.000100
Manhattan          0.000100
Name: proportion, Length: 531, dtype: float64

Code

super_df['Region'].value_counts(normalize=True)

Region
West       0.320492
East       0.284971
Central    0.232439
South      0.162097
Name: proportion, dtype: float64

Code

super_df['Sub-Category'].value_counts(normalize=True)

Sub-Category
Binders        0.152391
Paper          0.137082
Furnishings    0.095757
Phones         0.088953
Storage        0.084651
Art            0.079648
Accessories    0.077547
Chairs         0.061737
Appliances     0.046628
Labels         0.036422
Tables         0.031919
Envelopes      0.025415
Bookcases      0.022814
Fasteners      0.021713
Supplies       0.019011
Machines       0.011507
Copiers        0.006804
Name: proportion, dtype: float64

0.6 Categorical Variables Summary

0.7 Ship Mode

The Standard Class is the most commonly used shipping method, representing approximately 59.7% of all orders. This reflects customer preference or company policy favoring cost-effective delivery. Second Class (19.5%) and First Class (15.4%) follow, while Same Day shipping is the least common at 5.4%, likely due to its higher cost or limited availability.

0.8 Segment

The Consumer segment dominates the customer base, accounting for around 52% of orders. Corporate customers represent about 30%, while the Home Office segment makes up the remaining 18%. This distribution suggests that the business primarily serves individual consumers, but corporate and home office clients still form significant portions of the market.

0.9 Region

The dataset is fairly balanced across regions, with the West holding the largest share at 32%, followed by the East at 28%, Central at 23%, and South at 16%. This regional distribution reflects the geographical spread of the business’s customer base.

0.10 City

The dataset includes orders from 531 unique cities. The top contributors are New York City (9.2%), Los Angeles (7.5%), Philadelphia (5.4%), San Francisco (5.1%), and Seattle (4.3%). The remaining cities each contribute a small fraction of the total orders, indicating a broad but uneven geographical coverage.

0.11 Sub-Category

Among product sub-categories: - Binders (15.2%) and Paper (13.7%) are the most frequently sold items, reflecting strong demand for basic office supplies. - Furnishings (9.6%), Phones (8.9%), and Storage (8.5%) also have notable shares. - Lower-volume categories include Machines (1.2%) and Copiers (0.7%), which may represent high-value but low-frequency purchases.

0.12 Category Distribution

Code

super_df_category = super_df.groupby('Category').size().reset_index(name = 'total')
super_df_category

	Category	total
0	Furniture	2121
1	Office Supplies	6026
2	Technology	1847

Code

fig = px.pie(super_df_category, names='Category', values='total', title='Overall Category Distribution', 
             color_discrete_sequence=['darkcyan','skyblue','#82EEFD'])
fig.update_layout(template="presentation", paper_bgcolor="rgba(0, 0, 0, 0)", plot_bgcolor ="rgba(0, 0, 0, 0)")
fig.show()
fig.write_image(os.path.join(results_dir,'distribution_category_pie_chart.jpg'))
fig.write_image(os.path.join(results_dir,'distribution_category_pie_chart.png'))
fig.write_html(os.path.join(results_dir,'distribution_category_pie_chart.html'))

This pie chart shows the overall category distribution:

Furniture: 2,121 transactions
Office Supplies: 6,026 transactions
Technology: 1,847 transactions

Office Supplies dominate the sales volume, accounting for the majority of transactions. Furniture and Technology make up smaller but significant portions of the total sales. This distribution highlights where customer demand is concentrated across product categories.

0.13 Sales Distribution by Category

Code

sales_by_category = super_df.groupby('Category')['Sales'].sum().reset_index()
sales_by_category

	Category	Sales
0	Furniture	741999.7953
1	Office Supplies	719047.0320
2	Technology	836154.0330

Code

fig = px.pie(sales_by_category, names='Category', values='Sales', title='Sales Distribution by Category', 
             color_discrete_sequence=['darkcyan','skyblue','#82EEFD'])
fig.update_layout(template="presentation", paper_bgcolor="rgba(0, 0, 0, 0)", plot_bgcolor ="rgba(0, 0, 0, 0)")
fig.show()
fig.write_image(os.path.join(results_dir,'sales_distribution_category_pie_chart.jpg'))
fig.write_image(os.path.join(results_dir,'sales_distribution_category_pie_chart.png'))
fig.write_html(os.path.join(results_dir,'sales_distribution_category_pie_chart.html'))

This pie chart shows the sales distribution by category:

Furniture: $741,999.80
Office Supplies: $719,047.03
Technology: $836,154.03

Technology leads in total sales value, followed closely by Furniture and Office Supplies. This distribution highlights the revenue contributions of each category and helps identify key drivers of overall sales performance.

0.14 Sales Distribution by Sub-Category

Code

sales_by_sub_category = super_df.groupby('Sub-Category')['Sales'].sum().reset_index().sort_values(by='Sales', ascending=True)
sales_by_sub_category

	Sub-Category	Sales
8	Fasteners	3024.2800
10	Labels	12486.3120
7	Envelopes	16476.4020
2	Art	27118.7920
15	Supplies	46673.5380
12	Paper	78479.2060
9	Furnishings	91705.1640
1	Appliances	107532.1610
4	Bookcases	114879.9963
6	Copiers	149528.0300
0	Accessories	167380.3180
11	Machines	189238.6310
3	Binders	203412.7330
16	Tables	206965.5320
14	Storage	223843.6080
5	Chairs	328449.1030
13	Phones	330007.0540

Code

fig = px.bar(sales_by_sub_category, 
             x ='Sales', 
             y ='Sub-Category', title='Sales Distribution by sub-Category', 
             color_discrete_sequence=['darkcyan'],
             orientation='h',
            height= 550,
             text = 'Sales',
            width= 900)
fig.update_traces(texttemplate='%{x:.0f}', textposition = 'inside')
fig.update_layout(template="presentation", paper_bgcolor="rgba(0, 0, 0, 0)", plot_bgcolor ="rgba(0, 0, 0, 0)",
                 margin = dict(l=150, r=10, t=50, b=50))
fig.show()
fig.write_image(os.path.join(results_dir,'sales_distribution_sub_category_bar_plot.jpg'))
fig.write_image(os.path.join(results_dir,'sales_distribution_sub_category_bar_plot.png'))
fig.write_html(os.path.join(results_dir,'sales_distribution_sub_category_bar_plot.html'))

This bar chart shows the sales distribution by product sub-category:

Fasteners: $3,024.28
Labels: $12,486.31
Envelopes: $16,476.40
Art: $27,118.79
Supplies: $46,673.54
Paper: $78,479.21
Furnishings: $91,705.16
Appliances: $107,532.16
Bookcases: $114,880.00
Copiers: $149,528.03
Accessories: $167,380.32
Machines: $189,238.63
Binders: $203,412.73
Tables: $206,965.53
Storage: $223,843.61
Chairs: $328,449.10
Phones: $330,007.05

The sales distribution highlights that Phones, Chairs, and Storage are among the highest revenue-generating sub-categories, while Fasteners and Labels contribute smaller portions. This insight helps prioritize inventory and marketing focus on the most lucrative product lines.

0.15 Sales Distribution by Ship Mode

Code

sales_by_sub_ship = super_df.groupby('Ship Mode')['Sales'].sum().reset_index().sort_values(by='Sales', ascending=True)
sales_by_sub_ship

	Ship Mode	Sales
1	Same Day	1.283631e+05
0	First Class	3.514284e+05
2	Second Class	4.591936e+05
3	Standard Class	1.358216e+06

Code

fig = px.bar(sales_by_sub_ship, 
             x='Ship Mode', 
              y='Sales', title='Sales Distribution by Ship Mode', 
             color_discrete_sequence=['darkcyan'],
             orientation='v',
             text='Sales'
            )
fig.update_traces(texttemplate='%{y:.0f}', textposition = 'inside')
fig.update_layout(template="presentation", paper_bgcolor="rgba(0, 0, 0, 0)", plot_bgcolor ="rgba(0, 0, 0, 0)",)
fig.show()
fig.write_image(os.path.join(results_dir,'sales_distribution_shipmode_bar_plot.jpg'))
fig.write_image(os.path.join(results_dir,'sales_distribution_shipmode_bar_plot.png'))
fig.write_html(os.path.join(results_dir,'sales_distribution_shipmode_bar_plot.html'))

This bar chart shows the sales distribution by Ship Mode:

Same Day: $128,363
First Class: $351,428
Second Class: $459,193
Standard Class: $1,358,216

The distribution indicates that Standard Class dominates sales volume by a large margin, followed by Second Class and First Class. Same Day shipping accounts for the smallest portion of sales, reflecting its limited use or higher cost.

0.16 Sales Distribution by Region

Code

sales_by_region = super_df.groupby('Region')['Sales'].sum().reset_index().sort_values(by='Sales', ascending=True)
sales_by_region

	Region	Sales
2	South	391721.9050
0	Central	501239.8908
1	East	678781.2400
3	West	725457.8245

Code

fig = px.bar(sales_by_region, 
             x='Sales', 
             y='Region', title='Sales Distribution by Region', 
             color_discrete_sequence=['darkcyan'],
             text='Sales'
            )
fig.update_layout(template="presentation", paper_bgcolor="rgba(0, 0, 0, 0)", plot_bgcolor ="rgba(0, 0, 0, 0)", margin = dict(l=150, r=10, t=50, b=50))
fig.update_traces(texttemplate='%{x:.0f}', textposition = 'inside')
fig.show()
fig.write_image(os.path.join(results_dir,'sales_distribution_region_bar_ploty.jpg'))
fig.write_image(os.path.join(results_dir,'sales_distribution_region__bar_ploty.png'))
fig.write_html(os.path.join(results_dir,'sales_distribution_region__bar_ploty.html'))

This bar chart shows the sales distribution by Region:

South: $391,721
Central: $501,239
East: $678,781
West: $725,457

The data shows that the West region generates the highest sales, followed closely by the East region. The Central and South regions contribute smaller but significant portions to total sales, highlighting regional differences in market size or customer demand.

1 Customer Segment Distribution

Code

customer_segment = super_df.groupby('Segment').size().reset_index(name = 'total')
customer_segment

	Segment	total
0	Consumer	5191
1	Corporate	3020
2	Home Office	1783

Code

fig = px.bar(customer_segment, 
             x ='Segment', 
             y ='total', title='Customer Segment Distribution', 
             color_discrete_sequence=['darkcyan'],
             orientation='v',
            height= 450,
             text = 'total',
            width= 600)
fig.update_traces(textposition = 'inside')
fig.update_layout(template="presentation", paper_bgcolor="rgba(0, 0, 0, 0)", plot_bgcolor ="rgba(0, 0, 0, 0)",
                 margin = dict(l=150, r=10, t=50, b=50))
fig.show()
fig.write_image(os.path.join(results_dir,'customersegment_distribution_bar_plot.jpg'))
fig.write_image(os.path.join(results_dir,'customersegment_distribution_bar_plot.png'))
fig.write_html(os.path.join(results_dir,'customersegment_distribution_bar_plot.html'))

1.1 Customer Segment Distribution

This bar chart shows the distribution of customers across segments:

Consumer: 5,191 customers
Corporate: 3,020 customers
Home Office: 1,783 customers

The Consumer segment represents the largest group, accounting for over half of the customer base. Corporate clients form a significant portion, while Home Office customers make up the smallest segment. This distribution provides insight into the primary market focus of the business.

2 Monthly Sales Distribution

Code

super_df['Order Date'] = pd.to_datetime(super_df['Order Date'])
super_df['Month'] = super_df['Order Date'].dt.month_name()
super_df['Month_num'] = super_df['Order Date'].dt.month
super_df['Month_num']

0       11
1       11
2        6
3       10
4       10
        ..
9989     1
9990     2
9991     2
9992     2
9993     5
Name: Month_num, Length: 9994, dtype: int32

Code

sales_by_month = super_df.groupby(['Month_num', 'Month'])['Sales'].sum().reset_index()
sales_by_month = sales_by_month.sort_values('Month_num')
sales_by_month

	Month_num	Month	Sales
0	1	January	94924.8356
1	2	February	59751.2514
2	3	March	205005.4888
3	4	April	137762.1286
4	5	May	155028.8117
5	6	June	152718.6793
6	7	July	147238.0970
7	8	August	159044.0630
8	9	September	307649.9457
9	10	October	200322.9847
10	11	November	352461.0710
11	12	December	325293.5035

Code

fig = px.line(
    sales_by_month,
    x='Month',
    y='Sales',
    title='Sales Trends Over Time',
    width= 1000,
    height= 600,
    markers=True
)

fig.update_traces(line=dict(color='darkcyan', width=2),)
fig.update_layout(
    xaxis_title='Month',
    yaxis_title='Total Sales',
    template='presentation',
     margin = dict(l=90, r=50)
)

fig.show()
fig.write_image(os.path.join(results_dir,'salesovertime_line_chart.jpg'))
fig.write_image(os.path.join(results_dir,'salesovertime_line_chart.png'))
fig.write_html(os.path.join(results_dir,'salesovertime_line_chart.html'))

2.1 Monthly Sales Distribution

This pie chart shows the distribution of total sales across months:

January: $94,924
February: $59,751
March: $205,005
April: $137,762
May: $155,028
June: $152,718
July: $147,238
August: $159,044
September: $307,649
October: $200,322
November: $352,461
December: $325,293

The distribution shows that November and December generate the highest sales, reflecting seasonal peaks, likely driven by year-end promotions and holiday shopping. September also stands out as a strong sales month. In contrast, February and January have the lowest sales, possibly due to post-holiday slowdowns. These trends highlight key periods for targeted marketing and inventory planning.